Combining frequency and time domain approaches to systems with multiple spike train input and output

A frequency domain approach and a time domain approach have been combined in an investigation of the behaviour of the primary and secondary endings of an isolated muscle spindle in response to the activity of two static fusimotor axons when the parent muscle is held at a fixed length and when it is subjected to random length changes. The frequency domain analysis has an associated error process which provides a measure of how well the input processes can be used to predict the output processes and is also used to specify how the interactions between the recorded processes contribute to this error. Without assuming stationarity of the input, the time domain approach uses a sequence of probability models of increasing complexity in which the number of input processes to the model is progressively increased. This feature of the time domain approach was used to identify a preferred direction of interaction between the processes underlying the generation of the activity of the primary and secondary endings. In the presence of fusimotor activity and dynamic length changes imposed on the muscle, it was shown that the activity of the primary and secondary endings carried different information about the effects of the inputs imposed on the muscle spindle. The results presented in this work emphasise that the analysis of the behaviour of complex systems benefits from a combination of frequency and time domain methods

A frequentist framework of inductive reasoning

Reacting against the limitation of statistics to decision procedures, R. A. Fisher proposed for inductive reasoning the use of the fiducial distribution, a parameter-space distribution of epistemological probability transferred directly from limiting relative frequencies rather than computed according to the Bayes update rule. The proposal is developed as follows using the confidence measure of a scalar parameter of interest. (With the restriction to one-dimensional parameter space, a confidence measure is essentially a fiducial probability distribution free of complications involving ancillary statistics.) A betting game establishes a sense in which confidence measures are the only reliable inferential probability distributions. The equality between the probabilities encoded in a confidence measure and the coverage rates of the corresponding confidence intervals ensures that the measure's rule for assigning confidence levels to hypotheses is uniquely minimax in the game. Although a confidence measure can be computed without any prior distribution, previous knowledge can be incorporated into confidence-based reasoning. To adjust a p-value or confidence interval for prior information, the confidence measure from the observed data can be combined with one or more independent confidence measures representing previous agent opinion. (The former confidence measure may correspond to a posterior distribution with frequentist matching of coverage probabilities.) The representation of subjective knowledge in terms of confidence measures rather than prior probability distributions preserves approximate frequentist validity.Comment: major revisio

Looking inside the black box : a theory-based process evaluation alongside a randomised controlled trial of printed educational materials (the Ontario printed educational message, OPEM) to improve referral and prescribing practices in primary care in Ontario, Canada

Background: Randomised controlled trials of implementation strategies tell us whether (or not) an intervention results in changes in professional behaviour but little about the causal mechanisms that produce any change. Theory-based process evaluations collect data on theoretical constructs alongside randomised trials to explore possible causal mechanisms and effect modifiers. This is similar to measuring intermediate endpoints in clinical trials to further understand the biological basis of any observed effects (for example, measuring lipid profiles alongside trials of lipid lowering drugs where the primary endpoint could be reduction in vascular related deaths). This study protocol describes a theory-based process evaluation alongside the Ontario Printed Educational Message (OPEM) trial. We hypothesize that the OPEM interventions are most likely to operate through changes in physicians' behavioural intentions due to improved attitudes or subjective norms with little or no change in perceived behavioural control. We will test this hypothesis using a well-validated social cognition model, the theory of planned behaviour (TPB) that incorporates these constructs. Methods/design: We will develop theory-based surveys using standard methods based upon the TPB for the second and third replications, and survey a subsample of Ontario family physicians from each arm of the trial two months before and six months after the dissemination of the index edition of informed, the evidence based newsletter used for the interventions. In the third replication, our study will converge with the "TRY-ME" protocol (a second study conducted alongside the OPEM trial), in which the content of educational messages was constructed using both standard methods and methods informed by psychological theory. We will modify Dillman's total design method to maximise response rates. Preliminary analyses will initially assess the internal reliability of the measures and use regression to explore the relationships between predictor and dependent variable (intention to advise diabetic patients to have annual retinopathy screening and to prescribe thiazide diuretics for first line treatment of uncomplicated hypertension). We will then compare groups using methods appropriate for comparing independent samples to determine whether there have been changes in the predicted constructs (attitudes, subjective norms, or intentions) across the study groups as hypothesised, and will assess the convergence between the process evaluation results and the main trial results.The OPEM trial and OPEM process evaluation are funded by the Canadian Institute of Health Research (CIHR). The OPEM process evaluation study was developed as part of the CIHR funded interdisciplinary capacity enhancement team KT-ICEBeRG. Gaston Godin, Jeremy Grimshaw and France Légaré hold Canada Research Chairs. Louise Lemyre holds an R.S. McLaughlin Research Chair

A general approach to simultaneous model fitting and variable elimination in response models for biological data with many more variables than observations

<p>Abstract</p> <p>Background</p> <p>With the advent of high throughput biotechnology data acquisition platforms such as micro arrays, SNP chips and mass spectrometers, data sets with many more variables than observations are now routinely being collected. Finding relationships between response variables of interest and variables in such data sets is an important problem akin to finding needles in a haystack. Whilst methods for a number of response types have been developed a general approach has been lacking.</p> <p>Results</p> <p>The major contribution of this paper is to present a unified methodology which allows many common (statistical) response models to be fitted to such data sets. The class of models includes virtually any model with a linear predictor in it, for example (but not limited to), multiclass logistic regression (classification), generalised linear models (regression) and survival models. A fast algorithm for finding sparse well fitting models is presented. The ideas are illustrated on real data sets with numbers of variables ranging from thousands to millions. R code implementing the ideas is available for download.</p> <p>Conclusion</p> <p>The method described in this paper enables existing work on response models when there are less variables than observations to be leveraged to the situation when there are many more variables than observations. It is a powerful approach to finding parsimonious models for such datasets. The method is capable of handling problems with millions of variables and a large variety of response types within the one framework. The method compares favourably to existing methods such as support vector machines and random forests, but has the advantage of not requiring separate variable selection steps. It is also works for data types which these methods were not designed to handle. The method usually produces very sparse models which make biological interpretation simpler and more focused.</p

Poverty and fever vulnerability in Nigeria: a multilevel analysis

<p>Abstract</p> <p>Background</p> <p>Malaria remains a major public health problem in Sub Saharan Africa, where widespread poverty also contribute to the burden of the disease. This study was designed to investigate the relationship between the prevalence of childhood fever and socioeconomic factors including poverty in Nigeria, and to examine these effects at the regional levels.</p> <p>Methods</p> <p>Determinants of fever in the last two weeks among children under five years were examined from the 25004 children records extracted from the Nigeria Demographic and Health Survey 2008 data set. A two-level random effects logistic model was fitted. </p> <p>Results</p> <p>About 16% of children reported having fever in the two weeks preceding the survey. The prevalence of fever was highest among children from the poorest households (17%), compared to 15.8% among the middle households and lowest among the wealthiest (13%) (p<0.0001). Of the 3,110 respondents who had bed nets in their households, 506(16.3%) children had fever, while 2,604(83.7%) did not. (p=0.082). In a multilevel model adjusting for demographic variables, fever was associated with rural place of residence (OR=1.27, p<0.0001, 95% CI: 1.16, 1.41), sex of child: female (OR=0.92, p=0.022, 95% CI: 0.859, 0.988) and all age categories (>6months), whereas the effect of wealth no longer reached statistical significance.</p> <p>Conclusion</p> <p>While, overall bednet possession was low, less fever was reported in households that possessed bednets. Malaria control strategies and interventions should be designed that will target the poor and make an impact on poverty. The mechanism through which wealth may affect malaria occurrence needs further investigation. </p

Physical Stress, Not Biotic Interactions, Preclude an Invasive Grass from Establishing in Forb-Dominated Salt Marshes

Biological invasions have become the focus of considerable concern and ecological research, yet the relative importance of abiotic and biotic factors in controlling the invasibility of habitats to exotic species is not well understood. Spartina species are highly invasive plants in coastal wetlands; however, studies on the factors that control the success or failure of Spartina invasions across multiple habitat types are rare and inconclusive.We examined the roles of physical stress and plant interactions in mediating the establishment of the smooth cordgrass, Spartina alterniflora, in a variety of coastal habitats in northern China. Field transplant experiments showed that cordgrass can invade mudflats and low estuarine marshes with low salinity and frequent flooding, but cannot survive in salt marshes and high estuarine marshes with hypersaline soils and infrequent flooding. The dominant native plant Suaeda salsa had neither competitive nor facilitative effects on cordgrass. A common garden experiment revealed that cordgrass performed significantly better when flooded every other day than when flooded weekly. These results suggest that physical stress rather than plant interactions limits cordgrass invasions in northern China.We conclude that Spartina invasions are likely to be constrained to tidal flats and low estuarine marshes in the Yellow River Delta. Due to harsh physical conditions, salt marshes and high estuarine marshes are unlikely to be invaded. These findings have implications for understanding Spartina invasions in northern China and on other coasts with similar biotic and abiotic environments

Mental health: A cause or consequence of injury? A population-based matched cohort study

BACKGROUND: While a number of studies report high prevalence of mental health problems among injured people, the temporal relationship between injury and mental health service use has not been established. This study aimed to quantify this relationship using 10 years of follow-up on a population-based cohort of hospitalised injured adults. METHODS: The Manitoba Injury Outcome Study is a retrospective population-based matched cohort study that utilised linked administrative data from Manitoba, Canada, to identify an inception cohort (1988–1991) of hospitalised injured cases (ICD-9-CM 800–995) aged 18–64 years (n = 21,032), which was matched to a non-injured population-based comparison group (n = 21,032). Pre-injury comorbidity and post-injury mental health data were obtained from hospital and physician claims records. Negative Binomial regression was used to estimate adjusted rate ratios (RRs) to measure associations between injury and mental health service use. RESULTS: Statistically significant differences in the rates of mental health service use were observed between the injured and non-injured, for the pre-injury year and every year of the follow-up period. The injured cohort had 6.56 times the rate of post-injury mental health hospitalisations (95% CI 5.87, 7.34) and 2.65 times the rate of post-injury mental health physician claims (95% CI 2.53, 2.77). Adjusting for comorbidities and pre-existing mental health service use reduced the hospitalisations RR to 3.24 (95% CI 2.92, 3.60) and the physician claims RR to 1.53 (95% CI 1.47, 1.59). CONCLUSION: These findings indicate the presence of pre-existing mental health conditions is a potential confounder when investigating injury as a risk factor for subsequent mental health problems. Collaboration with mental health professionals is important for injury prevention and care, with ongoing mental health support being a clearly indicated service need by injured people and their families. Public health policy relating to injury prevention and control needs to consider mental health strategies at the primary, secondary and tertiary level

The rationale and design of the antihypertensives and vascular, endothelial, and cognitive function (AVEC) trial in elderly hypertensives with early cognitive impairment: Role of the renin angiotensin system inhibition

<p>Abstract</p> <p>Background</p> <p>Prior evidence suggests that the renin angiotensin system and antihypertensives that inhibit this system play a role in cognitive, central vascular, and endothelial function. Our objective is to conduct a double-blind randomized controlled clinical trial, the antihypertensives and vascular, endothelial, and cognitive function (AVEC), to compare 1 year treatment of 3 antihypertensives (lisinopril, candesartan, or hydrochlorothiazide) in their effect on memory and executive function, cerebral blood flow, and central endothelial function of seniors with hypertension and early objective evidence of executive or memory impairments.</p> <p>Methods/Design</p> <p>The overall experimental design of the AVEC trial is a 3-arm double blind randomized controlled clinical trial. A total of 100 community eligible individuals (60 years or older) with hypertension and early cognitive impairment are being recruited from the greater Boston area and randomized to lisinopril, candesartan, or hydrochlorothiazide ("active control") for 12 months. The goal of the intervention is to achieve blood pressure control defined as SBP < 140 mm Hg and DBP < 90 mm Hg. Additional antihypertensives are added to achieve this goal if needed. Eligible participants are those with hypertension, defined as a blood pressure 140/90 mm Hg or greater, early cognitive impairment without dementia defined (10 or less out of 15 on the executive clock draw test or 1 standard deviation below the mean on the immediate memory subtest of the repeatable battery for the assessment of neuropsychological status and Mini-Mental-Status-exam >20 and without clinical diagnosis of dementia or Alzheimer's disease). Individuals who are currently receiving antihypertensives are eligible to participate if the participants and the primary care providers are willing to taper their antihypertensives. Participants undergo cognitive assessment, measurements of cerebral blood flow using Transcranial Doppler, and central endothelial function by measuring changes in cerebral blood flow in response to changes in end tidal carbon dioxide at baseline (off antihypertensives), 6, and 12 months. Our outcomes are change in cognitive function score (executive and memory), cerebral blood flow, and carbon dioxide cerebral vasoreactivity.</p> <p>Discussion</p> <p>The AVEC trial is the first study to explore impact of antihypertensives in those who are showing early evidence of cognitive difficulties that did not reach the threshold of dementia. Success of this trial will offer new therapeutic application of antihypertensives that inhibit the renin angiotensin system and new insights in the role of this system in aging.</p> <p>Trial Registration</p> <p>Clinicaltrials.gov NCT00605072</p

Hospital-level associations with 30-day patient mortality after cardiac surgery: a tutorial on the application and interpretation of marginal and multilevel logistic regression

Background: Marginal and multilevel logistic regression methods can estimate associations between hospital-level factors and patient-level 30-day mortality outcomes after cardiac surgery. However, it is not widely understood how the interpretation of hospital-level effects differs between these methods. Methods. The Australasian Society of Cardiac and Thoracic Surgeons (ASCTS) registry provided data on 32,354 patients undergoing cardiac surgery in 18 hospitals from 2001 to 2009. The logistic regression methods related 30-day mortality after surgery to hospital characteristics with concurrent adjustment for patient characteristics. Results: Hospital-level mortality rates varied from 1.0% to 4.1% of patients. Ordinary, marginal and multilevel regression methods differed with regard to point estimates and conclusions on statistical significance for hospital-level risk factors; ordinary logistic regression giving inappropriately narrow confidence intervals. The median odds ratio, MOR, from the multilevel model was 1.2 whereas ORs for most patient-level characteristics were of greater magnitude suggesting that unexplained between-hospital variation was not as relevant as patient-level characteristics for understanding mortality rates. For hospital-level characteristics in the multilevel model, 80% interval ORs, IOR-80%, supplemented the usual ORs from the logistic regression. The IOR-80% was (0.8 to 1.8) for academic affiliation and (0.6 to 1.3) for the median annual number of cardiac surgery procedures. The width of these intervals reflected the unexplained variation between hospitals in mortality rates; the inclusion of one in each interval suggested an inability to add meaningfully to explaining variation in mortality rates. Conclusions: Marginal and multilevel models take different approaches to account for correlation between patients within hospitals and they lead to different interpretations for hospital-level odds ratios. © 2012 Sanagou et al; licensee BioMed Central Ltd

Using quantile regression to investigate racial disparities in medication non-adherence

<p>Abstract</p> <p>Background</p> <p>Many studies have investigated racial/ethnic disparities in medication non-adherence in patients with type 2 diabetes using common measures such as medication possession ratio (MPR) or gaps between refills. All these measures including MPR are quasi-continuous and bounded and their distribution is usually skewed. Analysis of such measures using traditional regression methods that model mean changes in the dependent variable may fail to provide a full picture about differential patterns in non-adherence between groups.</p> <p>Methods</p> <p>A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM).</p> <p>Results</p> <p>Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant.</p> <p>Conclusion</p> <p>Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.</p